The present invention relates generally to the field of imaging systems. In particular, the invention relates to a method for iteratively reconstructing image data acquired from a computed tomography imaging system.
Computed Tomography (CT) scanners operate by projecting fan shaped or cone shaped X-ray beams through an object. The X-ray beams are generated by an X-ray source, and are generally collimated prior to passing through the object being scanned. The attenuated beams are then detected by a set of detector elements. Each detector element produces a signal based on the intensity of the attenuated X-ray beams, and these signals are processed to produce projection data, also called sinogram data. By using reconstruction techniques, such as filtered backprojection, useful images are formed from the projection data.
A computer is able to process and reconstruct images of the portions of the object responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are computed by processing a series of angularly displaced projection data. These data are then reconstructed to produce reconstructed images, which are typically displayed on a cathode ray tube, and may be printed or reproduced on film.
Direct reconstruction techniques, such as filtered backprojection are generally fast and computationally efficient, since they allow reconstruction of a three-dimensional image data set in a single reconstruction step. Unfortunately, most direct reconstruction techniques exhibit relatively poor image quality with a low contrast and a significant artifact level.
Iterative reconstruction techniques improve image quality through an iterative step. Iterative reconstruction techniques perform an initial reconstruction followed by iterative updates of the two or three-dimensional image data set until some threshold criteria are met. In particular, iterative reconstruction techniques reduce image noise for a given dose, or equivalently, reduce the dose required to achieve a given noise, have increased geometrical flexibility and are capable of modeling the physics of the acquisition, thereby increasing the robustness against artifacts.
However, iterative reconstruction techniques require enormous amounts of computation and are not useful in practice unless the volume to be reconstructed is small. In addition, iterative reconstruction techniques are much slower than direct reconstruction techniques, generally requiring 10-100 times the computational cost as compared to direct reconstruction techniques. Some known techniques for reducing the computational cost of iterative reconstruction techniques include ordered subsets, relaxation factors, and acceleration of the projector and back projector. However, these techniques, which can be applied in general to any iterative reconstruction technique known in the art, do not sufficiently reduce the computation time to enable routine use of iterative reconstruction.
An iterative reconstruction technique that effectively reduces the required computational cost per iteration and reduces the number of iterations by improving the convergence properties is therefore desired.